An empirical investigation of the adoption of mobile health applications: integrating big data and social media services

Factors impacting the adoption of big data in healthcare: A systematic literature review

BACKGROUND

The term "big data" refers to the vast volume, variety, and velocity of data generated from various sources-e.g., sensors, social media, and online platforms. Big data adoption within healthcare poses an intriguing possibility for improving patients' health, increasing operational efficiency, and enabling data-driven decision-making. Despite considerable interest in the adoption of big data in healthcare, empirical research assessing the factors impacting the adoption process is lacking. Therefore, this review aimed to investigate the literature using a systematic approach to explore the factors that affect big data adoption in healthcare.

METHODS

A systematic literature review was conducted. The methodical and thorough process of discovering, assessing, and synthesizing relevant studies provided a full review of the available data. Several databases were used for the information search. Most of the articles retrieved from the search came from popular medical research databases, such as Scopus, Taylor & Francis, ScienceDirect, Emerald Insights, PubMed, Springer, IEEE, MDPI, Google Scholar, ProQuest Central, ProQuest Public Health Database, and MEDLINE.

RESULTS AND CONCLUSION

The results of the systematic literature review indicated that several theoretical frameworks (including the technology acceptance model; the technology, organization, and environment framework; the interactive communication technology adoption model; diffusion of innovation theory; dynamic capabilities theory; and the absorptive capability framework) can be used to analyze and understand technology acceptance in healthcare. It is vital to consider the safety of electronic health records during the use of big data. Furthermore, several elements were found to determine technological acceptance, including environmental, technological, organizational, political, and regulatory factors.

Mapping Ethical Artificial Intelligence Policy Landscape: A Mixed Method Analysis

As more national governments adopt policies addressing the ethical implications of artificial intelligence, a comparative analysis of policy documents on these topics can provide valuable insights into emerging concerns and areas of shared importance. This study critically examines 57 policy documents pertaining to ethical AI originating from 24 distinct countries, employing a combination of computational text mining methods and qualitative content analysis. The primary objective is to methodically identify common themes throughout these policy documents and perform a comparative analysis of the ways in which various governments give priority to crucial matters. A total of nineteen topics were initially retrieved. Through an iterative coding process, six overarching themes were identified: principles, the protection of personal data, governmental roles and responsibilities, procedural guidelines, governance and monitoring mechanisms, and epistemological considerations. Furthermore, the research revealed 31 ethical dilemmas pertaining to AI that had been overlooked previously but are now emerging. These dilemmas have been referred to in different extents throughout the policy documents. This research makes a scholarly contribution to the expanding field of technology policy formulations at the national level by analyzing similarities and differences among countries. Furthermore, this analysis has practical ramifications for policymakers who are attempting to comprehend prevailing trends and potentially neglected domains that demand focus in the ever-evolving field of artificial intelligence.

Getting Connected to M-Health Technologies through a Meta-Analysis

The demand for mobile e-health technologies (m-health) continues with constant growth, stimulating the technological advancement of such devices. However, the customer needs to perceive the utility of these devices to incorporate them into their daily lives. Hence, this study aims to identify users' perceptions regarding the acceptance of m-health technologies based on a synthesis of meta-analysis studies on the subject in the literature. Using the relations and constructs proposed in the UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) technology acceptance model, the methodological approach utilized a meta-analysis to raise the effect of the main factors on the Behavioral Intention to Use m-health technologies. Furthermore, the model proposed also estimated the moderation effect of gender, age, and timeline variables on the UTAUT2 relations. In total, the meta-analysis utilized 84 different articles, which presented 376 estimations based on a sample of 31,609 respondents. The results indicate an overall compilation of the relations, as well as the primary factors and moderating variables that determine users' acceptance of the studied m-health systems.

Predicting User Satisfaction of Mobile Healthcare Services using Machine Learning

Outbreak of the COVID-19 leads to rapid development of the mobile healthcare services. Given that user satisfaction is of great significance in inducing marketing success in competition markets, this research explores and predicts user satisfaction with mobile healthcare services. Specifically, the current research aimed to design a machine learning model that predicts user satisfaction with healthcare services using big data from Google Play Store reviews and satisfaction ratings. By dealing with the sentimental features in online reviews with five classifiers, the authors find that Logistic regression with term frequency-inverse document frequency (TF-IDF) and XGBoost with Bag of words (BoW) have superior performances in predicting user satisfaction for healthcare services. Based on these results, the authors conclude that such user-generated texts as online reviews can be used to predict user satisfaction, and Logistic regression with TF-IDF and XGBoost with BoW can be prioritized for developing online review analysis platforms for healthcare service providers.

Drivers of digital transformation adoption: A weight and meta-analysis

The advent of the global pandemic has accelerated the growing need for product and service transformation, highlighting the emerging importance of technology and creating the opportunity to update the digital transformation (DT) domain through empirical-quantitative research. This weight and meta-analysis enabled the synthesis and integration of previous literature on the scope of individual DT adoption, evaluating the state of the art and filling a void on the subject. Athwart 88 studies and 99 datasets by international sources, our results demonstrate that attitude and satisfaction are relevant predictors of behavioral intentions and promising outcomes, including compatibility and personal innovativeness. Behavioral intentions, satisfaction, and habit are the best predictors for DT use. Usefulness and ease of use are critical for DT adoption intention and use, being moderated by individualism, as a cultural factor, human capital, and knowledge-technology, as innovation indicators. We present a conceptual model of promising and best predictors for future research on DT individual adoption.

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An update of digital transformation (DT) through a weight and meta-analysis.

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Contribution to DT literature by surpassing biases and limitations of size estimates.

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Identification of promising and best predictors for further DT adoption research.

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Behavioral intentions, satisfaction, and habit are best predictors for DT use.

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Usefulness and ease of use are pivotal, being moderated by culture and innovation.

Mapping research strands of ethics of artificial intelligence in healthcare: A bibliometric and content analysis

The growth of artificial intelligence in promoting healthcare is rapidly progressing. Notwithstanding its promising nature, however, AI in healthcare embodies certain ethical challenges as well. This research aims to delineate the most influential elements of scientific research on AI ethics in healthcare by conducting bibliometric, social network analysis, and cluster-based content analysis of scientific articles. Not only did the bibliometric analysis identify the most influential authors, countries, institutions, sources, and documents, but it also recognized four ethical concerns associated with 12 medical issues. These ethical categories are composed of normative, meta-ethics, epistemological and medical practice. The content analysis complemented this list of ethical categories and distinguished seven more ethical categories: ethics of relationships, medico-legal concerns, ethics of robots, ethics of ambient intelligence, patients' rights, physicians' rights, and ethics of predictive analytics. This analysis likewise identified 40 general research gaps in the literature and plausible future research strands. This analysis furthers conversations on the ethics of AI and associated emerging technologies such as nanotech and biotech in healthcare, hence, advances convergence research on the ethics of AI in healthcare. Practically, this research will provide a map for policymakers and AI engineers and scientists on what dimensions of AI-based medical interventions require stricter policies and guidelines and robust ethical design and development.

Acceptance and usage of mHealth technologies amid COVID-19 pandemic in a developing country: the UTAUT combined with situational constraint and health consciousness

Purpose

The COVID-19 pandemic has created a devastating effect on public health. As “social distance” is a highly effective preventive measure of this pandemic, close contact between a patient and physician is strongly discouraged where possible. Consequently, mobile health (mHealth) technology is taking its momentum to fulfil this gap. The purpose of this study is, therefore, to empirically investigate the moderating as well as the direct role of situational constraint and health consciousness in the unified theory of acceptance and use of technology (UTAUT) constructs to understand the adoption and use behavior of mHealth care services amid pandemic.

Design/methodology/approach

Data were collected from existing mHealth users using an online survey questionnaire in Bangladesh. SmartPLS 3.0 and SPSS 23.0 were used for partial least squares-structural equation modeling.

Findings

Situational constraint and health consciousness both have strong direct positive effects on both behavioral intention (in all models) and use behavior (in Models 2 and 3). Further, this study revealed that effort expectancy remains insignificant in both direct and interaction effects whereas social influence becomes insignificant in interaction effects from direct significant effect (Models 1 and 2). Besides, the study reported that the relationship between behavioral intention and use behavior is moderated by situational constraint.

Originality/value

To the best of the authors’ knowledge, this study is the first in terms of mHealth empirical investigation considering the current pandemic situation. The incorporation of the situational constraint and health consciousness into the UTAUT model provides a holistic framework to understand the influence of the adoption and use behavior of mHealth amid pandemic.

Could amantadine interfere with COVID-19 vaccines based on the LNP-mRNA platform?

INTRODUCTION

Amantadine is a well-known medication with indications in neurology and infectious diseases. It is currently FDA approved for Parkinson's disease, drug-induced extrapyramidal symptoms, and influenza.

METHODS

The article is the author's original research hypothesis.

RESULTS

Because more people are going to be vaccinated and additional similar vaccines are going to be introduced, we should take into consideration the potential of amantadine to interfere with LNP-mRNA COVID-19 vaccine delivery into the target cells.

CONCLUSIONS

A more cautious approach to the patients taking amantadine as far as vaccination utilizing LNP-mRNA platform should be considered.